Rapid tool for cell nanoarchitecture integrity assessment.

With the rapid increase and accessibility of high-resolution imaging technologies of cells, the interpretation of results relies more and more on the assumption that the three-dimensional integrity of the surrounding cellular landscape is not compromised by the experimental setup. However, the only available technology for directly probing the structural integrity of whole-cell preparations at the nanoscale is electron cryo-tomography, which is time-consuming, costly, and complex. We devised an accessible, inexpensive and reliable screening assay to quickly report on the compatibility of experimental protocols with preserving the structural integrity of whole-cell preparations at the nanoscale. Our Rapid Cell Integrity Assessment (RCIA) assay is executed at room temperature and relies solely on light microscopy imaging. Using cellular electron cryo-tomography as a benchmark, we verify that RCIA accurately unveils the adverse impact of reagents and/or protocols such as those used for virus inactivation or to arrest dynamic processes on the cellular nanoarchitecture.

Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography.

Cryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require scoring functions to measure and accurately rank the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. Here, we provide the first study to assess the effectiveness of more than 15 scoring functions for evaluating the quality of subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis showed that the robustness of scoring functions varies largely. Most scores were sensitive to the signal-to-noise ratio of subtomograms and often required Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, showed a robust ranking of subtomogram clusters without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing an accurate scoring function for template-free approaches to detect complexes from heterogeneous samples.

TomoMiner and TomoMinerCloud: A Software Platform for Large-Scale Subtomogram Structural Analysis.

Cryo-electron tomography (cryo-ET) captures the 3D electron density distribution of macromolecular complexes in close to native state. With the rapid advance of cryo-ET acquisition technologies, it is possible to generate large numbers (>100,000) of subtomograms, each containing a macromolecular complex. Often, these subtomograms represent a heterogeneous sample due to variations in the structure and composition of a complex in situ form or because particles are a mixture of different complexes. In this case subtomograms must be classified. However, classification of large numbers of subtomograms is a time-intensive task and often a limiting bottleneck. This paper introduces an open source software platform, TomoMiner, for large-scale subtomogram classification, template matching, subtomogram averaging, and alignment. Its scalable and robust parallel processing allows efficient classification of tens to hundreds of thousands of subtomograms. In addition, TomoMiner provides a pre-configured TomoMinerCloud computing service permitting users without sufficient computing resources instant access to TomoMiners high-performance features.

Simulating cryo electron tomograms of crowded cell cytoplasm for assessment of automated particle picking.

BACKGROUND: Cryo-electron tomography is an important tool to study structures of macromolecular complexes in close to native states. A whole cell cryo electron tomogram contains structural information of all its macromolecular complexes. However, extracting this information remains challenging, and relies on sophisticated image processing, in particular for template-free particle extraction, classification and averaging. To develop these methods it is crucial to realistically simulate tomograms of crowded cellular environments, which can then serve as ground truth models for assessing and optimizing methods for detection of complexes in cell tomograms. RESULTS: We present a framework to generate crowded mixtures of macromolecular complexes for realistically simulating cryo electron tomograms including noise and image distortions due to the missing-wedge effects. Simulated tomograms are then used for assessing the template-free Difference-of-Gaussian (DoG) particle-picking method to detect complexes of different shapes and sizes under various crowding and noise levels. We identified DoG parameter settings that maximize precision and recall for detecting particles over a wide range of sizes and shapes. We observed that medium sized DoG scaling factors showed the overall best performance. To further improve performance, we propose a combination strategy for integrating results from multiple parameter settings. With increasing macromolecular crowding levels, the precision of particle picking remained relatively high, while the recall was dramatically reduced, which limits the detection of sufficient copy numbers of complexes in a crowded environment. Over a wide range of increasing noise levels, the DoG particle picking performance remained stable, but dramatically reduced beyond a specific noise threshold. CONCLUSIONS: Automatic and reference-free particle picking is an important first step in a visual proteomics analysis of cell tomograms. However, cell cytoplasm is highly crowded, which makes particle detection challenging. It is therefore important to test particle-picking methods in a realistic crowded setting. Here, we present a framework for simulating tomograms of cellular environments at high crowding levels and assess the DoG particle picking method. We determined optimal parameter settings to maximize the performance of the DoG particle-picking method.

Exploring the theoretical basis and limitations of cryo-STEM tomography for thick biological specimens.

Scanning transmission electron microscope (STEM) imaging has recently been applied to the cryo-tomography of thick biological specimens. As previously shown for plastic sections, STEM has a number of advantages for cryo-imaging compared to conventional wide-field TEM imaging. STEM is insensitive to phase coherence and is therefore suitable for much thicker specimens than TEM. Imaging in focus, with a long depth of field, also circumvents the complications of an oscillatory contrast transfer function and missing information at low spatial frequencies. Moreover the image signal represents a quantitative measurement of the electron scattering pixel by pixel, so that absolute intensities can be interpreted in terms of material properties in the specimen. Resolution, however, is undoubtedly compromised for thick samples, especially in the regime of multiple elastic scattering. In this work we address the specific issues that arise in cryo-tomography of thick biological specimens. We formulate an imaging model based on a Boltzmann transport equation, complemented by Monte Carlo simulations. Using these theoretical tools, we identify conditions for image acquisition that will be compatible with the basic presumption of tomographic reconstruction, i.e., that for a given composition the imaging signal varies monotonically with thickness. For optimal resolution, contrast, and signal strength, we propose to generalize the on-axis bright field detector to collect at angles well beyond the illumination cone. Our results justify the generation of 3D images for micron thicknesses and beyond.

Progressive Stochastic Reconstruction Technique (PSRT) for cryo electron tomography.

Cryo Electron Tomography (cryoET) plays an essential role in Structural Biology, as it is the only technique that allows to study the structure of large macromolecular complexes in their close to native environment in situ. The reconstruction methods currently in use, such as Weighted Back Projection (WBP) or Simultaneous Iterative Reconstruction Technique (SIRT), deliver noisy and low-contrast reconstructions, which complicates the application of high-resolution protocols, such as Subtomogram Averaging (SA). We propose a Progressive Stochastic Reconstruction Technique (PSRT) - a novel iterative approach to tomographic reconstruction in cryoET based on Monte Carlo random walks guided by Metropolis-Hastings sampling strategy. We design a progressive reconstruction scheme to suit the conditions present in cryoET and apply it successfully to reconstructions of macromolecular complexes from both synthetic and experimental datasets. We show how to integrate PSRT into SA, where it provides an elegant solution to the region-of-interest problem and delivers high-contrast reconstructions that significantly improve template-based localization without any loss of high-resolution structural information. Furthermore, the locality of SA is exploited to design an importance sampling scheme which significantly speeds up the otherwise slow Monte Carlo approach. Finally, we design a new memory efficient solution for the specimen-level interior problem of cryoET, removing all associated artifacts.

Template-free detection of macromolecular complexes in cryo electron tomograms.

MOTIVATION: Cryo electron tomography (CryoET) produces 3D density maps of biological specimen in its near native states. Applied to small cells, cryoET produces 3D snapshots of the cellular distributions of large complexes. However, retrieving this information is non-trivial due to the low resolution and low signal-to-noise ratio in tomograms. Current pattern recognition methods identify complexes by matching known structures to the cryo electron tomogram. However, so far only a small fraction of all protein complexes have been structurally resolved. It is, therefore, of great importance to develop template-free methods for the discovery of previously unknown protein complexes in cryo electron tomograms. RESULTS: Here, we have developed an inference method for the template-free discovery of frequently occurring protein complexes in cryo electron tomograms. We provide a first proof-of-principle of the approach and assess its applicability using realistically simulated tomograms, allowing for the inclusion of noise and distortions due to missing wedge and electron optical factors. Our method is a step toward the template-free discovery of the shapes, abundance and spatial distributions of previously unknown macromolecular complexes in whole cell tomograms. CONTACT: alber@usc.edu

Dual-axis electron tomography of biological specimens: Extending the limits of specimen thickness with bright-field STEM imaging.

The absence of imaging lenses after the specimen in the scanning transmission electron microscope (STEM) enables electron tomography to be performed in the STEM mode on micrometer-thick plastic-embedded specimens without the deleterious effect of chromatic aberration, which limits spatial resolution and signal-to-noise ratio in conventional TEM. Using Monte Carlo calculations to simulate electron scattering from gold nanoparticles situated at the top and bottom surfaces of a plastic section, we assess the optimal acquisition strategy for axial bright-field STEM electron tomography at a beam-energy of 300keV. Dual tilt-axis STEM tomography with optimized axial bight-field detector geometry is demonstrated by application to micrometer-thick sections of beta cells from mouse pancreatic islet. The quality of the resulting three-dimensional reconstructions is comparable to that obtained from much thinner (0.3-micrometer) sections using conventional TEM tomography. The increased range of specimen thickness accessible to axial STEM tomography without the need for serial sectioning enables the 3-D visualization of more complex and larger subcellular structures.

Monte Carlo electron-trajectory simulations in bright-field and dark-field STEM: implications for tomography of thick biological sections.

A Monte Carlo electron-trajectory calculation has been implemented to assess the optimal detector configuration for scanning transmission electron microscopy (STEM) tomography of thick biological sections. By modeling specimens containing 2 and 3 at% osmium in a carbon matrix, it was found that for 1-microm-thick samples the bright-field (BF) and annular dark-field (ADF) signals give similar contrast and signal-to-noise ratio provided the ADF inner angle and BF outer angle are chosen optimally. Spatial resolution in STEM imaging of thick sections is compromised by multiple elastic scattering which results in a spread of scattering angles and thus a spread in lateral distances of the electrons leaving the bottom surface. However, the simulations reveal that a large fraction of these multiply scattered electrons are excluded from the BF detector, which results in higher spatial resolution in BF than in high-angle ADF images for objects situated towards the bottom of the sample. The calculations imply that STEM electron tomography of thick sections should be performed using a BF rather than an ADF detector. This advantage was verified by recording simultaneous BF and high-angle ADF STEM tomographic tilt series from a stained 600-nm-thick section of C. elegans. It was found that loss of spatial resolution occurred markedly at the bottom surface of the specimen in the ADF STEM but significantly less in the BF STEM tomographic reconstruction. Our results indicate that it might be feasible to use BF STEM tomography to determine the 3D structure of whole eukaryotic microorganisms prepared by freeze-substitution, embedding, and sectioning.